{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "! [ -e /content ] && pip install -Uqq fastbook\n",
    "import fastbook\n",
    "fastbook.setup_book()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-25T03:23:03.529302Z",
     "start_time": "2025-06-25T03:23:03.526940Z"
    }
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "from fastbook import *\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开启深度学习之旅"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您好，无论您掌握到什么程度，我们都深感荣幸可以加入到您的深度学习之旅中来！在本章，我们会告诉您更多关于这本书的内容，包括介绍深度学习的关键概念，以及在不同的任务中训练我们的首个模型。即便您没有相关的技术背景或者数学基础也没有任何关系（有的话也可以）。撰写本书的目的是希望让尽可能多的人可以接触到深度学习。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习适合所有人"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很多人认为想要在深度学习上取得很好的成果，需要各种难以获取的资源，但是通过学习本教程您会发现这种想法是错误的。下表列举了几项你绝对不需要做世界级深度学习的内容。\n",
    "\n",
    "| AI神话（不需要） |                     事实                     |\n",
    "|:---------:| :------------------------------------------: |\n",
    "| 大量专业数学知识  |         高中数学就足够了         |\n",
    "|   海量数据    | 我们曾见过用不到50条的数据取得破纪录结果 |\n",
    "|  大量昂贵的算力  |        你可以免费获得最先进的工作成果        |\n",
    "\n",
    "\n",
    "深度学习是一项利用多层神经网络进行数据提取与转换的计算机技术，应用场景从人类语音识别到动物图像分类。每层神经网络从它的前序神经网络层获取输入并不断完善。算法训练这些层，从而减少错误提高精度。通过这种方式，神经网络学习完成特定的任务。在下一节中我们将详细讨论训练算法。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "深度学习具有强大、灵活、简单的特点。 正因为如此，我们相信可以在许多学科应用它。其中包括社会科学和自然科学 、艺术、医学、金融、科学研究等等。举个例子，杰里米尽管没有任何医学背景，但他创建了 Enlitic 公司，该公司利用深度学习算法来诊断疾病。公司成立几个月后，他宣布其算法可以[比放射科医生更准确](https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html)地识别恶性肿瘤。\n",
    "\n",
    "利用深度学习或大量使用深度学习方法进行处理的任务有很多很多，以下列出了其中的一些领域，都是目前世界上最优的 ：\n",
    "- 自然语言处理（NLP）：答疑；语音识别；文档汇总；文档分类；在文档中查找姓名、日期等；搜索提及某一概念的文章\n",
    "- 计算机视觉：卫星和无人机图像判读（例如，抗灾能力）、面部识别、图像标注、交通标志识别、自动驾驶中定位行人和车辆\n",
    "- 医药：发现放射学图像中的异常，包括CT、MRI和X射线图像；病理切片计数特征；超声波的测量特性；诊断糖尿病视网膜病变\n",
    "- 生物：蛋白质折叠；蛋白质分类；许多基因组学任务，例如肿瘤正常测序和分类临床可操作的基因突变分类：细胞分类；蛋白质分析/蛋白质交互作用\n",
    "- 图像生成：图像着色，增强图像分辨率，图像去噪，将图像转换成著名艺术家风格的艺术作品\n",
    "- 推荐系统：网页搜索，产品推荐，主页布局\n",
    "- 玩游戏：国际象棋，围棋，大多数雅达利电子游戏，及许多即时战略游戏\n",
    "- 机器人：处理难以定位（例如，透明的、有光泽的、缺乏纹理的）或难以拾取的物体\n",
    "- 其他应用：金融和物流预测，文本转语音，等等......\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "值得注意的是，深度学习有各种各样的应用，但几乎所有的深度学习都基于一种创新模型：神经网络。\n",
    "\n",
    "然而，事实上神经网络并不是全新的事物。为了对本领域有更加深入的认识，有必要先介绍一些它的历史。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络简史"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1943年，神经生理学家沃伦·麦卡洛克（Warren McCulloch）和逻辑学家沃尔特·皮茨（Walter Pitts）合作开发了人工神经元的数学模型。在他们的[论文](https://link.springer.com/article/10.1007/BF02478259)《神经活动中内在思想的逻辑微积分》中，他们认为：\n",
    "> 由于神经活动的\"全有或全无\"特点，神经事件及其间的关系可以被看做命题逻辑。每个网络的行为都可以用这种方式描述。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "他们意识到一个真实神经元的简化模型可以用简单的加法和阈值来表示，如下图<<神经元>>所示。皮茨是自学成才的，12岁时，他收到了剑桥大学的邀请，跟随伟大的伯特兰·罗素教授学习。他没有接受这一邀请，事实上，在他的一生中，他没有接受任何高级学位或权威职位的邀请。他的大部分著名工作都是在他无家可归的时候完成的。尽管他缺乏官方认可的职位，且越来越与社会隔绝，但他与麦卡洛克的工作成果却很有影响力，并由一位名叫弗兰克·罗森布拉特的心理学家将他的研究继续进行下来。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\" align=\"left\">\n",
    "  <p align=\"center\">\n",
    "    <img src=\"./images/chapter1_neuron.png\" alt=\"neuor\" style=\"zoom:85%;\" />\n",
    "  </p>\n",
    "  <p align=\"center\">图：神经元</p>\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "罗森布拉特进一步开发了人工神经元，使其具有学习能力。更重要的是，他致力于建造第一个使用这些原理的设备，即Mark I感知器。在《智能自动机的设计》一书中，罗森布拉特写到了这项工作：“我们现在即将见证这样一台机器的诞生——一台不需要任何人类训练或控制就能感知、辨认和识别周围环境的机器。”感知器被建造出来，并能够成功识别简单的形状。\n",
    "\n",
    "一位名叫马文·明斯基（Marvin Minsky）的麻省理工学院教授（和罗森布拉特在同一所高中仅仅比他低一个年级！）和西摩·帕佩特（Seymour Papert）一起写了一本关于罗森布拉特发明的书，名为《感知器》（Perceptrons）（麻省理工学院出版社）。他们表示，这些设备的单层无法学习一些简单但关键的数学函数（如XOR）。在此书中，他们还表明，使用多层设备将使这些限制得到解决。不幸的是，这些见解中只有第一个得到了广泛认可。因此，在接下来的二十多年，全球学术界几乎完全放弃了神经网络。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在过去50年中，神经网络领域最关键的工作可能是由David Rumelhart、James McClelland和PDP研究小组，于1986年由麻省理工学院出版发布的多卷并行分布式处理（PDP）。第一章展示了与罗森布拉特类似的期望：\n",
    "> 人类比当今的计算机更聪明，因为大脑采用了一种基本的计算架构，这种架构更适合处理人类擅长的自然信息处理任务的核心方面。……我们将介绍一个用于模拟认知过程的计算框架，该框架似乎比其他框架更接近大脑可能进行的计算方式。\n",
    ">\n",
    "在此使用PDP的前提是传统计算机程序的工作方式与大脑非常不同，这可能就是为什么计算机程序（在这一点上）在做一些大脑认为轻而易举的事情时会如此糟糕（例如识别图片中的物体）的原因。作者声称PDP方法“比其他框架更接近”大脑的工作方式，因此它或许能够更好地处理这类任务。\n",
    "\n",
    "事实上，PDP中的方法与当今神经网络中使用的方法非常相似。该书将并行分布式处理定义为以下需求：\n",
    "   1. 一组*处理单元*\n",
    "   2. *激活*状态\n",
    "   3. 每个单元的*输出函数*\n",
    "   4. 单元之间的*连接模式*\n",
    "   5. 用于通过连接性网络传播活动模式的*传播规则*\n",
    "   6. 一种*激活规则*，用于将作用于单元的输入与该单元的当前状态相结合，以产生该单元的输出\n",
    "   7. 通过经验修改连接模式的一种*学习规则*\n",
    "   8. 必要的系统运行*环境*\n",
    "\n",
    "我们将在本书中看到现代神经网络处理上面的每个需求。\n",
    "\n",
    "在20世纪80年代，大多数模型都是用两层神经元构建的，从而避免了明斯基和帕佩特认识到的问题（也就是采用上述架构时被他们称做的“单元之间的连接模式”）。事实上，神经网络在80年代和90年代被广泛用于实际项目。然而，理论问题的错误理解再次阻碍了该领域的发展。理论上，仅仅增加一层额外的神经元层就足以用这些神经网络估计任何数学函数。但在实践中，这样的网络通常太大太慢而无法使用。\n",
    "\n",
    "尽管研究人员在30年前就表明，要获得实用、良好的性能，你需要使用更多层的神经元，但是仅在近十年里，这一原则才被更广泛的重视和应用。由于使用了更多的层，再加上计算机硬件的提升、数据可用性的增加以及算法的调整使得神经网络更快、更容易被训练，神经网络终于实现了其潜力。我们现在实现了罗森布拉特所设想的：“一台在没有任何人类训练或控制的情况下，能够感知、识别和辨认其周围环境的机器。”\n",
    "\n",
    "这将是在本书中您学习如何实现的内容。因为我们将会共同度过很长一段时间，首先让我们互相了解一下...\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 我们是谁"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们是西尔万和杰里米，是您本次学习之旅的向导，希望您认为我们很称职。\n",
    "\n",
    "杰里米已经使用和讲授机器学习大约30年了。他在25年前就开始使用神经网络。在此期间，他领导了许多以机器学习为核心的公司和项目，包括创立了第一个专注于深度学习和医学的公司Enlitic，并担任全球最大的机器学习社区Kaggle的总裁和首席科学家。他和Rachel Thomas博士是fast.ai的联合创始人，进而创建了本书所立足的课程。\n",
    "\n",
    "您将时常在边栏中看到我们，比如这个来自杰里米的：\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 杰里米: 大家好，我是杰里米！了解了我的学习背景你可能会大跌眼镜，我其实并没有接受过任何正规的技术教育。我曾经获得了哲学专业的学士学位，但成绩不是很好。我更有兴趣做实际项目，而不是理论研究，所以整个大学时期我都在一家名为麦肯锡的管理咨询公司做全职工作。假如你是一个宁愿亲自动手建造东西而不是花几年时间学习抽象概念的人，你就会理解我的意思了！像我这样具有较少数学或正式技术背景的人，可以通过留意我的边栏内容，去获取最适合你的信息。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相反西尔万对正规技术教育非常了解。他写了10本数学教科书，涵盖了整个法国高级数学课程"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 西尔万: 与杰里米不同，我没有花很多年的时间编写代码和应用机器学习算法。最近通过观看杰里米的Fast.AI视频课程进入了机器学习世界。所以，如果你是一个没有打开过终端，并输入命令行的人，你就会理解我的过往了！像我一样具有更多数学或正式技术背景，但实际编程经验较少的人，请留意我的边栏，获取适合你的信息。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "来自世界各地各行各业的数十万名学生学习了fast.ai课程。西尔万作为最令杰里米印象深刻的学生从中脱颖而出，这使他加入了fast.ai，然后与杰里米一起成为fastai软件库的合著者。\n",
    "\n",
    "这些意味着，你同时拥有两个方面的优势：比任何人都更了解这个软件的人，因为他们编写了它，其中一位是数学家，别一位是编码和机器学习方面的专家；同时，他们既能理解数学领域的门外汉，也能理解编码和机器学习领域的门外汉。\n",
    "\n",
    "看过体育节目的人都知道，如果解说员团队是两个人，那么还需要第三个人来做“特别评论”。我们的特别评论员是亚历克西斯·加拉格尔。亚历克西斯有着非常多元化的背景：他做过数学生物学研究员、编剧、即兴表演者、麦肯锡顾问（就像杰里米一样！）、Swift程序员和首席技术官。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 亚历克西斯:\n",
    "> 我决定是时候学习人工智能了！毕竟，我几乎什么都试过了。但我并没有构建机器学习模型的背景。嗯...这会有多难呢？就像你们一样，我将通过这本书来进行学习。请留意我边栏中的学习技巧，它们对我的学习很有帮助，希望能够对您也有所帮助。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何学习深度学习"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "《全局学习法》（Making Learning Whole，Jossey-Bass）作者，哈佛大学（Harvard）教授戴维·珀金斯（David Perkins）对教学工作有其见解。其基本的思路是讲授整体。意思是，如果你在教棒球，你首先要带人们去看棒球比赛，或者让他们去玩棒球。你不会教他们如何从零开始缠绕麻线来制作棒球，也不会教他们抛物线的物理性质，或者球在球棒上的摩擦系数。\n",
    "\n",
    "哥伦比亚大学数学博士，前布朗大学教授，K-12数学教师保罗·洛克哈特（Paul Lockhart）写了篇颇具影响力[文章](https://www.maa.org/external_archive/devlin/LockhartsLament.pdf)“数学家的悲叹”，这篇文章想象了用讲授数学的方式去教音乐和美术时噩梦般的世界【它想象一个噩梦般的世界在用讲授数学的方式去教音乐和美术】。孩子们不允许听或演奏音乐，除非他们花了十多年的时间掌握乐谱和乐理，在课堂上把乐谱换成不同的音调。在美术课上，学生们学习颜色和涂抹器，但在大学之前却不允许真正的去画画。这听起来很荒谬吗？这恰恰是数学的教学方式——我们要求学生花数年时间死记硬背，枯燥学习、不连贯的基础知识，我们声称这些将在今后得到回报，但是等得太久以至于大多数人放弃了这门学科。\n",
    "\n",
    "不幸的是，这正是许多深度学习教学资源的方式 —— 要求学习者学习海森定义和损失函数的泰勒近似，但是从来没有真实工作的代码事例。我们不是在批评微积分。我们喜欢微积分，西尔万甚至还在大学里教过它，但我们不认为它是学习深度学习的最佳起点！\n",
    "\n",
    "在深度学习中，如果你有动力去修正你的模型，让它变得更好，将是非常有帮助的。即，开始学习相关理论的时候，首先需要有模型。我们对每个知识点都尽量用实例讲授。通过这些示例，逐步深入的向你展示如何使你的项目越来越好。这意味着你将会逐步学习到需要的所有理论基础，以这种方式学习，你会知道它的重要性与工作方式。\n",
    "\n",
    "下面是我们对你的承诺。在本书中，我们会遵循以如原则：\n",
    "  - 讲授*整体*。首先，我们将向您展示如何通过简单明了的工具使用一个完整的、有效的、可用的、最先进的深度学习网络来解决实际问题。然后，我们将逐渐深入理解那些工具是如何制作的，以及制作那些工具的工具是如何制作的，等等...\n",
    "  - 实例化教学。我们将确保提供可以让您直观理解的环境和效果，而不是从代数符号开始。\n",
    "  - 尽可能的简化。我们花费很多年的时间来构建工具和教学方法，以使用以前很复杂主题变得简单。\n",
    "  - 消除障碍。到目前为止，深度学习一直是一款独家游戏。我们正在开放它，确保每个人都能玩。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "深度学习最难的部分是实践：你如何知道你是否有足够的数据，它们的格式是否正确，你的模型是否训练得当，如果不是，你应该怎么做？这就是为什么我们信奉要在实践中学习的原因。与基础数据科学技能一样，深度学习只有通过实践经验才能学的更好。试图在理论上花费太多时间可能会适得其反。关键是要编码并尝试解决问题：在你具备背景知识和学习动机之前，可以暂时先不学习理论知识。\n",
    "\n",
    "有时这个学习之旅会很艰难。当你卡壳的时候。不要放弃！倒回去，找到你没有被卡住的最后的地方，从那里慢慢读下去，找到第一个不清楚的地方。然后自己尝试做一些代码实验，并搜索更多关于你卡壳问题的教程 —— 通常你会从不同的角度找到可能会有所帮助的材料。此外，在第一次阅读时不理解所有内容（尤其是代码）也是正常的。尝试连续理解材料有时会很困难。有时候，当你从后续内容学习到更多的背景知识，对内容有了一个更全面的了解，当前的问题就会迎刃而解了。因此，如果您确实在某个部分卡住了，无论如何都要试着继续前进，记好笔记以后再回来解决。\n",
    "\n",
    "记住，你不需要任何特定的学术背景就能在深度学习中取得成功。许多重要的突破都是由没有博士学位的人在研究和实践中取得的，比如这篇论文[“基于深度卷积生成对抗网络的无监督表示学习”](https://arxiv.org/abs/1511.06434)，这是过去十年中最具影响力的论文之一，引用次数超过5000次，是亚历克·雷德福（Alec Radford）在读本科的时候撰写的。即使在特斯拉，他们正在努力解决制造自动驾驶汽车这一极其艰巨的挑战，首席执行官埃隆·马斯克说:\n",
    "> “绝对不要求必须具有博士学位。重要的是对人工智能的深刻理解和具备采用切实有效的方法来实现神经网络的能力（后者才是真正的难点）。高中毕业也无所谓的。”\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而，要想成功，你需要做的是将你在本书中学到的东西应用到个人项目中，并始终坚持下去。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 你的项目和你的心态"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无论你是兴奋地从叶子的图片中识别植物是否患病，自动的生成编织图案，通过X光片诊断结核病，还是确定何时浣熊正在使用你的猫洞，我们会尽快的让你在自己的问题上使用深度学习（通过别人的预训练模型），然后逐步深入到更多细节。在下一章的前30分钟内，您将学习如何使用深度学习以最先进的准确度解决自己的问题（如果您渴望立即开始编写代码，可以直接跳过这部分）。不是需要拥有谷歌公司那种级别的计算资源和数据集才能够进行深度学习。\n",
    "\n",
    "那么，什么样的任务才是好的实验案例呢？你可以训练你的模型区分毕加索和莫奈的画作，或者选出你女儿的照片而不是你儿子的照片。专注于你的爱好和激情是非常有帮助的。在开始的时候最好是给自己设定四五个小项目，而不是努力解决一个大问题。过早地雄心勃勃往往会事与愿违，反而容易陷入困境。一旦你掌握了基础知识，把目标放到完成你真正引以为豪的事情上！\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 杰里米: 深度学习几乎可以解决任何问题。例如，我的第一家创业公司是一家名为FastMail的公司，该公司在1999年成立时提供增强的电子邮件服务（直到今天仍然如此）。2002年，我使用一种早期形式的深度学习，即单层神经网络，来帮助对电子邮件进行分类，并阻止客户收到垃圾邮件。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在深度学习上做得好的人的共同特性都包含爱玩和好奇心强。已故物理学家理查德·费曼（Richard Feynman）就是一个我们认为擅长深度学习的例子：他从餐碟在空中旋转时摇摆的乐趣中理解了亚原子粒子运动。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们关注你将学到的内容，从软件开始。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 软件：PyTorch, fastai 和 Jupyter\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（以及为什么无关紧要）"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们已经使用数十个软件包和许多编程语言完成了数百个机器学习项目。在fast.ai，我们用现有的大多数主要深度学习和机器学习软件包编写了课程。2017年PyTorch问世后，我们花了一千多个小时测试它，然后决定将它用于未来的课程、软件开发和研究。从那时起，PyTorch已经成为世界上增长最快的深度学习库，并且已经被用于顶级会议的大多数研究论文。这通常是工业界的主要指标，因为这些论文成果最终会应用在商业产品和服务中。我们发现PyTorch是深度学习的最灵活和最具表现力的库。它并没有为了权衡速度而简单，而是两者兼备。\n",
    "\n",
    "PyTorch是最好的底层基础库，可以很好的为高层功能提供基本操作。fastai库是在PyTorch基础上添加这些高级功能的最流行的库。它也特别适合本书的目标，因为它在提供深层软件架构方面是独一无二的（甚至有一篇关于此分层API的[同行评议学术论文](https://arxiv.org/abs/2002.04688)）。在这本书中，随着我们越来越深入地研究深度学习的原理，我们也将越来越深入地研究fastai的各个层。这本书涵盖了第2版的fastai库，这是一个从零开始的重写，可以提供许多独特的功能的库。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而，你学什么软件并不重要，因为只需要几天时间就能学会从一个库切换到另一个库。真正重要的是学习正确的深度学习原理和技术。我们聚焦在使用代码尽可能清晰地表达您需要学习的概念。在我们讲授高等概念的地方，我们会使用高层fastai代码。在我们讲授底层概念的地方，我们将使用底层PyTorch库，甚至纯Python代码。\n",
    "\n",
    "现如今新的深度学习库正在不断快速涌现，但你需要为未来几个月和几年更快的变化速度做好准备。随着越来越多的人进入这个领域，他们会带来更多的技术和想法，并且尝试更多的事情。你应该设想你今天学到的任何特定的库和软件在一两年内都会被淘汰。就想想在网络编程世界中软件库和技术栈发生的变化数量吧，这还是一个比深度学习更成熟、增长更缓慢的领域。我们坚信，学习的重点需要放在理解底层技术，和如何在实践中应用它们，以及如何在新工具和技术发布时快速构建专业知识。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本书结束时，您将了解fastai中几乎所有的代码（以及PyTorch的大部分代码），因为在每一章中，我们都将更深入地向您展示当我们构建和训练模型时到底发生了什么。这意味着你将学会现代深度学习中最重要的最佳实践——不仅仅是如何使用它们，而是知道它们真正的工作原理和实现。如果您想在另一个技术框架中使用这些方法，您将拥有必备的知识。\n",
    "\n",
    "因为学习深度学习最重要的事件是写代码和做实验。你有一个好的平台来做代码实验是很重要的。我们将在本书中使用最流行的编程实验平台--Jupyter。我们将向您展示如何使用Jupyter来训练和试验模型，并反思数据预处理和模型开发流水线的每个阶段。由于[Jupyter Notebook](https://jupyter.org/)功能强大、灵活且易于使用，它是最流行的Python数据科学工具，我们相信你会喜欢它的！"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们在实践中学习并训练我们的第一个模型吧。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 你的第一个模型"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如我们之前所说的，我们会先教你如何去做，然后再解释为什么这样做。按照这种自上而下的方法，我们将开始实际训练一个识别猫和狗的图像分类器，它有着近乎100%的准确率。要训练这个模型并运行我们的实验，您需要进行一些初始设置。别担心，这并不像看起来那么难。 【它没有看起那么难】"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 西尔万：不要跳过设置部分，即使它一开始看起来很有难度，特别是如果您很少或没有使用终端或命令行的经验。实际上大部分设置工作都不是必需的，您会发现最简单的服务器只需使用您常用的Web浏览器即可设置。为了学习，与本书同步运行自己的实验是至关重要的。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取一个图形处理（GPU）深度学习服务器"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要完成本书中的实验，您最好有一台配备了英伟达（NVIDIA）图形处理器（GPU）的计算机（其它品牌的GPU不能完全支持主要的深度学习库）。但是，我们不建议您购买。事实上，即使你已经有了一个，我们也不建议你使用它！配置计算机需要耗费大量的时间和精力，你现在需要把所有的精力都集中在深度学习本身。因此，我们建议您租用一台已经预装了您所需的一切随时可用的云服务器。使用费用可以低至每小时几毛钱，有些选项甚至是免费的。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 术语：图形处理器（GPU）。也称为图形卡。计算机中的一种特殊处理器，可同时处理数千个单一任务，专为在计算机上显示3D环境玩游戏而设计。这些相同的基本任务和神经网络非常类似，因此GPU运行神经网络的速度比普通CPU快数百倍。所有新型计算机都有GPU，但很少有适合深度学习所需的GPU.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大多数平台只需要大约两分钟就可以完成设置，很多平台不需要支付任何费用，甚至不需要信用卡就可以开始使用。\n",
    "- 九天・毕昇平台：中国移动推出的 AI 算力平台，注册即送 1000-3000 算力豆，可免费使用 V100 显卡（显存 32GB），支持 Jupyter/VSCode 开发环境，终端有 root 权限，适合深度学习训练。但算力豆有效期较短，需注意资源消耗，且平台维护较频繁。\n",
    "- 阿里天池实验室：阿里云旗下平台，提供 60 小时免费 GPU 时长，配备 V100、P100、T4 显卡，支持 Notebook 在线调试，适合短期训练和竞赛。限制是单次最长使用 8 小时，需合理分配时间。[阿里云服务](https://free.aliyun.com/?spm=5176.30234224.J_4NWEMkQ5nDwOgLi8EJmHs.8.523944d3YVd3J2&product=1395&crowd=personal)\n",
    "- 百度 AI Studio：每周提供数十小时免费 GPU 算力，使用 Tesla V100 显卡，适合 PaddlePaddle 框架用户，内置丰富数据集和教程。不过仅支持百度自研框架，无 root 权限，安装其他框架困难。\n",
    "- 腾讯云高性能工作空间（Cloud Studio）：每月有 5 万分钟免费时长，配置为 T4 显卡（16G 显存）+8 核 CPU+32G 内存，支持在线 VSCode 开发，适合 AI 应用部署。\n",
    "- 华为云 ModelArts：提供 NVIDIA Tesla V100 免费实例，支持 TensorFlow、PyTorch 等框架，适合 AI 模型训练与部署。\n",
    "- Kaggle Notebook：CPU 内存升级至 29GB，每周免费 30 小时 GPU 时长，单次训练最长 12 小时，超时自动终止，适合数据科学和机器学习项目开发。\n",
    "- Google Colab：动态分配 NVIDIA T4、P100、V100 等显卡，显存通常为 15-16GB，支持 CUDA 加速。免费用户单次最长使用 12 小时，空闲超时 30 分钟自动断开。\n",
    "\n",
    "> 亚历克西斯：我的两点看法：注意这个建议！如果你喜欢电脑，你会被诱惑去创建自己的工具箱。当心！这是虽然可行的，但是非常复杂和分神。这本书之所以没有被命名为 _《关于Ubuntu系统管理、NVIDIA驱动安装、apt-get、conda、pip和Jupyter Notebook配置的一切》_ 是有充分理由的。那将是另外的一本书。我在工作中设计并部署了我们的机器学习基础设施，我可以证明这有满足感，但与建模无关，就像维护一架飞机与驾驶一架飞机无关一样。\n",
    "\n",
    "完成设置后，您会看到如下图所示的页面。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Initial view of Jupyter Notebook\" width=\"658\" caption=\"Initial view of Jupyter Notebook\" id=\"notebook_init\" src=\"images/att_00057.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，您可以运行您的第一个Jupyter了！"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 术语：Jupyter 是一款允许你在单个文档中进行交互的软件，包含格式化的文本、代码、图像、视频等。由于Jupyter在学术领域和工业界的广泛应用和巨大影响，它获得了软件的最高荣誉——ACM软件系统奖。Jupyter 被数据科学家广泛用于深度学习模型的开发和交互的软件。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运行你的第一个 Jupyter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter 按照和本书相同的章节顺序编号。因此，您将看到列出的第一个Jupyter就是您现在要使用的。你将使用它来训练狗和猫图像分类模型。你会下载一个狗和猫图像的数据集，并使用它来训练模型。数据集是一组数据，它可以是图像、电子邮件、财务指标、声音或其他任何东西。有许多用免费数据集适合用来训练模型。这些数据集有的是由学者创建的，用以帮助推进研究，有的是为了比赛而提供的（数据科学家可以通过比赛看谁训练出最准确的模型！），还有一些是其他流程的副产品（如财务文件）。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 注：完整版和精简版的Jupyter。两个文件夹包含的Jupyter文件版本不同。*完整版*包含用于创建你现在正在阅读的本书内容Jupyter，它带有所有的练习和输出。*精简版*有相同的标题和代码单元，但去掉了输出和练习。在阅读一个章节后，我们建议您合上本书，仔细阅读精简版Jupyter，看看你是否能在执行前弄清楚每个单元将显示什么。并试着回忆代码演示的内容。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "点击一个Jupyter文件并打开它，如下图所示（请注意，不同平台之间的细节可能略有不同；您可以忽略这些差异）。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"An example of notebook\" width=\"700\" caption=\"A Jupyter notebook\" src=\"images/0_jupyter.png\" id=\"jupyter\"/>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter由单元组成。有两种主要类型的单元：\n",
    "- 包含格式化文本、图像等的单元。使用一种名为*Markdown*语言格式，你很快就会学到。\n",
    "- 包含可以执行的代码的单元，可以立即在下面显示输出（可以是纯文本、表格、图像、动画、声音，甚至是交互式应用）。\n",
    "\n",
    "Jupyter可以是两种模式之一：编辑模式或命令模式。在编辑模式下，用键盘以常用的方式将字母输入到单元中。然而在命令模式下，你不会看到任何闪烁的光标，键盘上的每个键对应特定功能。\n",
    "\n",
    "在继续之前，请按键盘上的Esc键切换到命令模式（如果您已经处于命令模式，则不会执行任何操作，因此为了以防万一现在按Esc键）。要查看的完整功能列表，请按 H 键，按Esc可以退出帮助界面。请注意，与大多数程序不同的是，在命令模式下，执行命令不需要您按住Control、Alt或类似的键，您只要按下所需的字母键即可。\n",
    "\n",
    "您可以通过按 C 键复制单元（需要先选择单元，用环绕的边框来表示；如果尚未选中，请单击）。然后按 V 键粘贴它的副本。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单击并选中以“# CLICK ME”行开头的单元。该行第一个字符表示本行的后续内容是Python代码的注释，因此在执行单元时将省略它。信不信由你，其余部分是一个创建和训练最先进的识别猫或狗模型的完整系统。所以，让我们现在就开始训练吧！只需按键盘上的Shift-Enter组合键，或单击工具栏上的Play按钮，然后等待几分钟，会发生以下事项：\n",
    "\n",
    "1. 一个包含37个品种猫和狗的7349张图像，名为[牛津-IIIT宠物数据集](http://www.robots.ox.ac.uk/~vgg/data/pets/)（oxford-iiit-pet.tgz）将从fast.ai数据集下载到您正在使用的GPU服务器，然后将被提取。「默认存放目录：\\~/.fastai/archive」\n",
    "2. 从互联网上下载一个*预训练的模型（resnet34-b627a593.pth）*，该模型已经用竞赛获奖模型对130万张图像进行了训练。「预训练模型存入目录：\\~/.cache/torch/hub/checkpoints/」\n",
    "3. 预训练模型将使用最新的迁移学习进行*微调*，来创建一个专门为识别狗和猫而定制的模型。\n",
    "\n",
    "\n",
    "前两个步骤只在GPU服务器上执行一次。如果您再次运行该单元，它将使用已经下载的数据集和模型，而不需要重复下载。让我们来看看单元的内容和模型训练的输出结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.177122</td>\n",
       "      <td>0.028925</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>00:38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.039905</td>\n",
       "      <td>0.022291</td>\n",
       "      <td>0.005413</td>\n",
       "      <td>00:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#id first_training\n",
    "#caption Results from the first training\n",
    "# CLICK ME\n",
    "from fastai.vision.all import *\n",
    "path = untar_data(URLs.PETS)/'images'\n",
    "\n",
    "def is_cat(x): return x[0].isupper()\n",
    "dls = ImageDataLoaders.from_name_func(\n",
    "    path, get_image_files(path), valid_pct=0.2, seed=42,\n",
    "    label_func=is_cat, item_tfms=Resize(224))\n",
    "\n",
    "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fine_tune(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您看到的结果与此处显示的不一定完全相同。在训练模型中存在许多小的随机变化。在这个例子中，我们的误差率一般会远小于0.02。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 名词解释：训练时间。根据您的网络速度，下载预训练模型和数据集可能需要几分钟时间。`微调`可能也需要一分钟左右的时间。通常训练本书中的模型或你自己的模型需要几分钟的时间。当你训练模型时，为了充分利用这段时间，您可以继续阅读下一节，或者打开另一个Jupyter文件做编码实验。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 补充内容：这本书是用Jupyter编写的"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们用Jupyter编写了这本书，所以本书中几乎每个图、表与公式计算我们都会向您展示确切的代码，以便于您可以亲自复现。这就是为什么在本书中，您经常会看到紧跟着表格、图片或文本的代码。如果去本书的[官网](https://book.fast.ai)，您将找到所有代码，并且可以尝试自己运行和修改每个示例。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您刚才看到了单元如何输出书中的表格。下面是一个输出文本的单元示例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter 始终打印或显示最后一行的结果（如果有的话）。例如，下面是一个输出图像的单元示例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=151x192>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = PILImage.create(image_cat())\n",
    "img.to_thumb(192)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 补充内容结束"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那么，我们怎么知道这个模型好不好呢？在表的最后一列中，您可以看到错误率，即被错误识别的图像比例。错误率作为我们对模型质量的衡量标准是直观的和可理解的。正如你所看到的，这个模型几乎是完美的，而且训练时间只有几秒钟（不包括下载数据集和预训练模型所需要的时间）。事实上，您的准确率远好于10年前任何人的结果！\n",
    "\n",
    "最后，让我们检查一下这个模型是否真的有效。去找一张狗或猫的照片。如果身边没有，可以搜索图片并下载下来。现在执行定义好的上传代码模块，它会输出一个可以点击的按钮 `uploader`，你可以选择想要分类的图像：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c886ebd7920242fb97b87bd0950f2099",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_output\n",
    "uploader = widgets.FileUpload()\n",
    "uploader"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"An upload button\" width=\"159\" id=\"upload\" src=\"images/att_00008.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，您可以把上传的文件给模型。确保它是一只狗或一只猫的清晰照片，而不是线条、卡通或类似的东西。Jupyter会告诉您它认为这是一只狗还是一只猫，以及它有多确定。希望你觉得你的模型做得很棒："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# For the book, we can't actually click an upload button, so we fake it\n",
    "uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is this a cat?: True.\n",
      "Probability it's a cat: 1.000000\n"
     ]
    }
   ],
   "source": [
    "img = PILImage.create(uploader.data[0])\n",
    "is_cat,_,probs = learn.predict(img)\n",
    "print(f\"Is this a cat?: {is_cat}.\")\n",
    "print(f\"Probability it's a cat: {probs[1].item():.6f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "恭喜你完成了你的第一个分类器！\n",
    "\n",
    "但这意味着什么呢？您到底做了些什么工作呢？为了解释这一点，让我们把视野放大, 从全局来看看。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 什么是机器学习"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您的分类器是一个深度学习模型。正如之前提到过的，深度学习模型使用神经网络，神经网络起源于20世纪50年代，得益于最近的发展，开始变得非常强大。\n",
    "\n",
    "另一个关键的背景是，深度学习只是机器学习这一更广泛学科中的一个现代领域。在理解训练自己的分类模型本质时，其实并不需要理解深度学习。一般来说，看看你的模型和训练过程是如何应用机器学习概念的例子就足够了。\n",
    "\n",
    "所以在这一部分，我们将描述什么是机器学习。我们会探索关键的概念，并追溯介绍它们的原始论文。\n",
    "\n",
    "机器学习和常规程序一样，是让计算机完成一种特定任务。但是，我们如何使用常规编程来完成我们在上一节中所做的事情：识别照片中的狗和猫？我们必须为计算机写下完成任务所需的确切步骤。\n",
    "\n",
    "通常，当我们编写程序时，写下完成任务的步骤对我们来说很容易。只需思考我们手工完成任务的步骤，然后将其转换为代码。例如，我们写一个对列表进行排序的功能。通常，我们会编写一个如下图\\<basic_program\\>所示流程的功能（其中输入可能是未排序的列表，而结果是排序后的列表）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       "<!-- Generated by graphviz version 13.0.1 (20250615.1724)\n",
       " -->\n",
       "<!-- Title: G Pages: 1 -->\n",
       "<svg width=\"278pt\" height=\"58pt\"\n",
       " viewBox=\"0.00 0.00 278.00 58.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 54.4)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-54.4 273.87,-54.4 273.87,4 -4,4\"/>\n",
       "<!-- 程序 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>程序</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"170.93,-50.4 102.93,-50.4 98.93,-46.4 98.93,0 166.93,0 170.93,-4 170.93,-50.4\"/>\n",
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       "<!-- 结果 -->\n",
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       "<title>程序&#45;&gt;结果</title>\n",
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       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"31.47\" y=\"-22.03\" font-family=\"Times,serif\" font-size=\"14.00\">输入</text>\n",
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   "source": [
    "#hide_input\n",
    "#caption A traditional program\n",
    "#id basic_program\n",
    "#alt Pipeline inputs, program, results\n",
    "# ubuntu ：sudo apt-get install graphviz\n",
    "# win: https://graphviz.org/download/\n",
    "gv('''程序[shape=box3d width=1 height=0.7]  输入->程序->结果''')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但对于识别照片中的物体来说，这有点棘手。当我们识别图片中的物体时，将会采取什么步骤？我们真的不知道，因为这一切都是无意识的发生在我们的大脑中。\n",
    "\n",
    "早在1949年计算机刚刚诞生的时候，一位名叫亚瑟·塞缪尔的IBM研究员就开始研究让计算机完成任务的不同方法，他称之为机器学习。在他1962年的经典论文《人工智能：自动化前沿》中，他写道："
   ]
  },
  {
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   "metadata": {},
   "source": [
    "> 为这样的计算编写计算机程序是一项困难的任务，不仅仅因为计算机自身内在的复杂性，而是因为需要以令人恼火的细节详细说明该过程的每一个细小步骤。正像任何程序员都会告诉你的那样，计算机是巨大的白痴，而不是巨大的大脑。"
   ]
  },
  {
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   "source": [
    "他的基本想法是这样的：不是告诉计算机解决问题所需的确切步骤，而是向它展示所解决问题的例子，让它弄清楚如何自己去解决这个问题。这被证明是非常有效的：到1961年，他的跳棋程序通过这种方法学习很多内容，甚至击败了康涅狄格州的冠军！下面是他描述的思路（同样出自前面提到的文章）："
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  {
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    "> 假设我们安排一些自动手段，根据实际性能以测试任何当前权重分配的有效性，并提供改变权重分配的机制，以最大限度地提高性能。我们不需要深入研究这种程序的细节，就可以看到它能够完全自动化，也可以看到这样编程的机器会从它的经验中“学习”。"
   ]
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   "source": [
    "在这个简短的声明中包含了许多强有力的概念：\n",
    "- “权重分配”的想法\n",
    "- 事实上，每个权重分配都有一些“实际性能”\n",
    "- 要求有一种测试该性能的“自动化手段”\n",
    "- 需要一个“机制”（即另外的自动化过程）通过改变权重分配弄改进性能。\n",
    "\n",
    "让我们逐一了解这些概念，以便了解它们在实践中是如何组合在一起的。首先，我们需要理解塞缪尔所说的“权重分配”是什么意思。\n",
    "\n",
    "权重就是变量，权重分配就是为这些变量选择特定的值。程序的输入是为了产生结果而处理的值——例如，将图像像素作为输入，会返回“狗”分类作为结果。程序的权重分配是定义程序如何运行的其他值。\n",
    "\n",
    "因为它们会影响程序，它们从某种意义上说是另一种输入。考虑到这一点，我们将我们的基础图\\<basic_program\\>替换为图\\<weight_assignment\\>。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
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   ],
   "source": [
    "#hide_input\n",
    "#caption A program using weight assignment\n",
    "#id weight_assignment\n",
    "gv('''模型[shape=box3d width=1 height=0.7]\n",
    "输入->模型->结果; 权重->模型''')"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们已将方框的名称从“程序”更改为“模型”。这是为了遵循现代术语，并且可以反映出模型是一种特殊的程序：根据权重不同，它可以完成许多不同的事情。可以通过许多不同的方式来实现。例如，在塞缪尔的跳棋程序中，不同的权重值将产生不同玩跳棋的策略。\n",
    "\n",
    "（顺便提一下，以防您遇到那个术语，塞缪尔所说的“权重”现在通常被称为模型*参数*。而*权重*是为特定类型的模型参数保留的。）\n",
    "\n",
    "接下来，塞缪尔说*我们需要一种自动化手段根据实际性能以测试任何当前权重分配的有效性*。以他的跳棋程序为例，模型的“实际性能”将是它玩得有多好。你可以通过让两个模型互为对手来自动测试两个模型的性能，看哪一个模型赢得多，\n",
    "\n",
    "最后，他说，我们需要*一种能够调整权重分配以便取得最好性能的机制*。例如，我们可以查看获胜模型和失败模型之间的权重差异，进一步调整获胜方面的权重。\n",
    "\n",
    "我们现在可以明白*为什么他说这种程序可以完全自动化，而且……这样编程的机器会从它的经验中“学习”*。当权重的调整也是自动化的时候，学习将变得完全自动化 —— 当我们不是通过手动调整权重来改进模型，是依赖于性能进行调整的自动化机制。\n",
    "\n",
    "下图\\<training_loop\\>展示了塞缪尔训练一个机器学习模型想法的全貌。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "source": [
    "#hide_input\n",
    "#caption Training a machine learning model\n",
    "#id training_loop\n",
    "#alt The basic training loop\n",
    "gv('''ordering=in\n",
    "模型[shape=box3d width=1 height=0.7]\n",
    "输入->模型->结果; 权重->模型; 结果->性能\n",
    "性能->权重[constraint=false label=更新]''')"
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   "source": [
    "注意模型的结果（即跳棋游戏中的走法）与其性能（即它是否赢得游戏，或者它赢得有多快）之间的区别。\n",
    "\n",
    "还要注意的是，一旦模型被训练 —— 即，一旦我们选择了最终的、最好的、最喜欢的权重分配，那么我们就可以将权重视为模型的一部分，因为我们不会再变更它们了。\n",
    "\n",
    "因此，实际使用一个训练完成的模型，如下图\\<using_model\\>所示.\n"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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       "<!-- 结果 -->\n",
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       "<title>结果</title>\n",
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       "<title>模型&#45;&gt;结果</title>\n",
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       "<title>输入&#45;&gt;模型</title>\n",
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "#caption Using a trained model as a program\n",
    "#id using_model\n",
    "gv('''模型[shape=box3d width=1 height=0.7]\n",
    "输入->模型->结果''')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这与我们最初的流程图\\<basic_program\\>看起来相同，只是把“程序”一词替用“模型”替换。这是一个重要的理解：可以像对待常规计算机程序一样对待训练过的模型。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 术语：机器学习。允许计算机从经验中学习而开发的训练程序，而不是手动编码每个步骤。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 什么是神经网络"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不难想象跳棋程序的模型会是什么样子。它可能会是一系列跳棋策略的编码，以及某种搜索机制，并且权重可以因策略的选取而变，在搜索期间关注棋盘的哪些部分等等。但是，对于图像识别，理解文本，或者我们能想象到的许多其他有趣的问题，模型可能是什么样子并不是那么明显。\n",
    "\n",
    "我们想要某种功能非常灵活，只需改变它的权重就可以用来解决任何给定的问题。令人惊讶的是，这种功能真的存在！就是我们已经讨论过的“神经网络”。也就是说，如果你把神经网络看作是数学函数，那么它就是一个基于权重的极度灵活的功能。被称为*普遍近似定理*的数学证明显示该功能，理论上可以解决任何问题的精准性水平。神经网络如此灵活的事实意味着，在实践中，它们通常是一种合适的模型，您可以将精力集中在训练它们的过程上——也就是说，找到好的重量分配。\n",
    "\n",
    "但这个过程是怎样的呢？可以想象，您可能需要找到一种新的“机制”来自动的为每个问题更新权重，这将是相当费力的。我们想要的是一种完全通用的方法来更新神经网络的权重，使其改进任何给定的任务。很幸运这也是存在的！\n",
    "\n",
    "这被称为随机梯度下降（SGD）。我们将在第四章看到神经网络和SGD工作细节，及普遍近似定理的解释。然而，现在，我们将使用塞缪尔自己的话：我们不需要深入这种程序的细节，就看出它可以完全自动化，并且看到这样编程的机器能够从它的经验中“学习”。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 杰里米说：别担心，无论SGD和神经网络在数学上都不复杂。两者几乎完全依靠加法和乘法去做他们的工作（做了*大量*的加法和乘法运算！）。我们听到当学生看到细节时主要反应是：“这就是全部吗？”"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "换句话说，总结一下。一个神经网络就是一个特定的机器学习模型，正好符合塞缪尔的最初开概念。神经网络之所以特别因为他们是高度灵活的，这意味着只要找到正确的权重，他们就能够解决异常广泛的问题。这很强大，因为随机梯度下降给我们了提供了一种能够自动找到那些权重值的方法。\n",
    "\n",
    "现在我们先从宏观视角抽离出来，再重新聚焦，运用塞缪尔的框架重新审视我们的图像分类问题。\n",
    "\n",
    "我们输入的是图像，我们的权重是神经网络中的权重，我们的模型是神经网络，我们的结果是由神经网络计算出的值，如“狗”或“猫”。\n",
    "\n",
    "那下一步*一种自动化措施，依据实际性能测试当前权重分配的有效性*呢？确定“实际性能”很容易：我们可以简单地将模型的性能定义为预测正确回答的准确率。\n",
    "\n",
    "把所有内容组合起来，假设 SGD 是我们用来更新权重分配的机制，我们能够明白，我们的图像分类器是一个怎样的机器学习模型，更符合塞缪尔的设想。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一些机器学习术语"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "塞缪尔工作于20世纪60年代，在那之后专有名词已经发生了变化。以下是所有我们之前讨论过的现代深度学习专有名词：\n",
    "- *模型*的功能形式被称为其*框架*（但要小心，有时人们把*模型*当作*框架*的同义词，所以这可能会令人困惑）。\n",
    "- *权重*被称为*参数*。\n",
    "- *预测*是根据*自变量*计算的，即*数据*不包括*标注*。\n",
    "- 模型的*结果*被称为*预测*。\n",
    "- *性能*的衡量标准被称为*损失*。\n",
    "- 损失不仅取决于预测，也取决于正确的*标注*（也被称为*目标*或*因变量*）。例如，“狗”或“猫”。\n",
    "\n",
    "进行这些改动后，我们的图\\<training_loop\\>就变成图\\<detailed_loop\\>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
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    "#hide_input\n",
    "#caption Detailed training loop\n",
    "#id detailed_loop\n",
    "gv('''ordering=in\n",
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    "损失->参数[constraint=false label=更新]''')"
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 机器学习因有的局限性\n",
    "\n",
    "现在我们从上图能够看到训练一个深度学习模型的一些基本内容：\n",
    "- 没有数据就无法创建模型。\n",
    "- 模型只能学习用于训练它的输入数据上看到的模式进行操作。\n",
    "- 这种学习方法只创建*预测*，而不是建议*行为*。\n",
    "- 仅仅有输入数据样例是不够的；我们也需要这些数据的*标注*（例如，狗和猫的图片不足以训练模型；我们需要每张图片的标注，说明哪些是狗，哪些是猫）。\n",
    "\n",
    "一般来说，我们看到大多数机构所说的他们没有足够多的数据，实际含义是他们没有足够*标注*的数据。如果任何机构有兴趣使用模型做些实践工作，那么他们可能会有一些输入，计划运行他们的模型。也可能他们已经用其他方法做了一段时间了（例如，手动，或者用一些启发式程序），所以他们会在那些过程中得到了数据！例如，放射学实践肯定会有医学扫描资料（因为他们需要检查病人病情随着时间进展情况），但这些资料可能没有包含诊断或干预措施列表的结构化标注信息（因为放射科医生一般会创建自由文本自然语言的报告，而不是结构化数据）。我们将在这本书中讨论一些标注方法，因为它在实践中是一个重要问题。\n",
    "\n",
    "由于此类机器学习模型只能做出预测（即，尝试复制标注），这会在机构的目标和模型的能力之间产生巨大差距。例如，在本书中，您会学习如何创建一个推荐系统，能预测用户可能购买的产品的。这通常用于电子商务，例如通过显示排名最高的产品来定制主页上显示的产品。但这样的模型通常是通过查看用户和他们购买的历史记录（*输入*）以及他们想要购买或查看的内容（*标注*）来创建的，这意味着该模型可能会告诉您用户已经拥有或已经了解的产品，而不是他们听说的最有可能感兴趣的新产品。与此不同的是，当地的书商专家会通过问问题来了解你的品味，然后推荐一些你以前从未听说过的作家或丛书。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个关键的见解来自于考虑模型与它的环境如何进行交互。这会产生*反馈循环*，如下所述：\n",
    "\n",
    "- 基于过去实施逮捕的地点创建了*一个预测性警务*模型。这实际上不是预测犯罪，而是预测逮捕，因此只简单反映了现有警务过程中的偏见。\n",
    "- 然后，执法人员利用这个模型来决定他们的警务活动的重点，导致增加那些地区的逮捕人数。\n",
    "- 然后这些额外逮捕的数据会反馈给未来模型版本的训练。\n",
    "这是一个*正反馈循环*，模型使用得越多，数据的偏见就越大，从而使模型的偏见更大，以此类推。\n",
    "\n",
    "反馈循环也会在商业环境中产生问题。例如，视频推荐系统可能偏向于推荐那些观看人数最多的视频（例如，阴谋论者和极端主义者倾向于观看比平均水平更多的在线视频），导致那些用户增加了他们视频的观看量，导致更多那类视频被推荐。我们将在第三章中更详细地考虑这个话题。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在您已经了解了理论的基础，让我们回到代码示例，详细看代码如何对应到我们刚才描述的流程。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我们图像识别器如何工作"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们看看我们的图像识别器代码和这些概念是如何关联的。我们将把每一行放到一个独立的单元格中，并查看每一行在做什么（我们还是不会解释每个参数的每个细节，但对比较重要的部分会给出描述；完整细节将在本书的后续章节介绍）。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "第一行导入所有fastai.vision库 。\n",
    "```python\n",
    "from fastai.vision.all import *\n",
    "```\n",
    "它为我们提供了创建各种计算机视觉模型所需的所有函数和类。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 杰里米：许多Python程序员建议避免以这种方式（使用`import *`语法）导入整个库，因为在大型软件项目中会引发出很多问题。然而，在Jupyter这样的交互式工作效果却很好。fastai库是专门为支持这种交互式使用而设计的，它只将必要的内容导入到你的运行环境中。"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "第二行从[fast.ai数据集](https://course.fast.ai/datasets)（如果之前未下载过）下载标准数据集到你的机器上，并将其解压（如果以前未解压过），返回包含解压缩位置的`路径`对象：\n",
    "```python\n",
    "path = untar_data(URL.PETS)/'images'\n",
    "```\n",
    "> 西尔万：在Fast.ai学习期间，即使到今天，我都学到了很多关于高效编码实践。fastai库和fast.ai 笔记中充满了很棒的小技巧帮助我成为更好的程序员。例如，请注意fastai库不是仅返回包含数据路径的字符串，而是`path`对象。这是来自Python3标准库的一个非常有用的类，它使访问文件和目录变得更加容易。如果您以前没有遇到过它，请务必查看它的文档或教程并尝试着去使用它。请注意，本书的[网址](https://book.fast.ai)包含指向每章推荐教程的链接。当我们遇到一些我发现有用的小编码技巧时，我会告诉你。"
   ]
  },
  {
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   "source": [
    "在第三行中，我们定义了一个函数`is_cat`，它根据数据集创建者提供的文件名规则来标注猫：\n",
    "```python\n",
    "def is_cat(x): return x[0].isupper()\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "我们在第四行中使用该函数，告诉fastai我们用什么类型的数据集以及它是如何构造的：\n",
    "```python\n",
    "dls = ImageDataLoaders.from_name_func(\n",
    "    path, get_image_files(path), valid_pct=0.2, seed=42,\n",
    "    label_func=is_cat, item_tfms=Resize(224))\n",
    "```\n",
    "对于不同类型的深度学习数据集和问题，有不同的类 —— 这里我们使用的是`ImageDataLoaders`。类名的第一部分通常是的数据类型，如图像或文本。\n",
    "\n",
    "另外一个重要内容是，我们必须告诉fastai如何从数据集中获取标注信息。计算机视觉数据集通常的结构是这样的，图像的标签是文件名或路径的一部分 —— 最常见的是父文件夹名。fastai附带了一些标准化的标注方法，以及允许写你自己的标注方法。在这里，我们让fastai使用我们刚刚定义的`is_cat`函数。\n",
    "\n",
    "最后，我们定义了所需的`变换`。`变换`包含在训练期间自动使用的代码；fastai包含着许多预定义的`变换`，并且添加新的`变换`就像创建Python函数一样简单。它们有两种类型：`item_tfms`应用于每张图像（在这个例子中，每张图像被为缩放为224*224像素的正方形），而`batch_tfms`使用GPU一次性处理一`批`图像，因此它们的速度特别快（我们将在本书中看到许多这样的例子）。\n",
    "\n",
    "为什么是224*224像素？由于历史性原因这是标准尺寸（老的预训练模型就需要这个大小），但你可以传递更大的尺寸。如果您增加尺寸，您通常会得到一个更好的模型（因为它能专注更多细节），但代价是速度和内存消耗。如果减小尺寸，则情况正好相反。\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "> 术语：分类和回归。分类和回归在机器学习中具有特定的含义。这是我们将在本书中研究的两种主要类型的模型。分类模型用来预测类别或分类的模型。即，它根据一些离散概率进行预测，例如“狗”或“猫”。回归模型用来预测一个或多个数值的模型，例如温度或位置。有时人们用*回归* 一词来代指一种称为*线性回归模型*特定模型。这是一种不好的做法，我们不会在本书中使用该术语。"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "宠物数据集包含7390张狗和猫的照片，由37个品种组成。每个图像都使用其文件名进行标注：例如，文件 *great_pyrenees_173.jpg* 是数据集中Great Pyrenees品种狗的第173个图像。如果图像是一只猫，则文件名以大写字母开头，反之则以小写字母开头。我们必须告诉 fastai 如何从文件名中获取标签，我们通过调用函数`from_name_funchan`（这表示着可以使用函数从文件名中提取标签）和传递`is_cat`函数，会返回`x[0].isupper()`的值，如果第一个字母是大写则评估主为`True`（即，它是只猫）。\n",
    "\n",
    "这里提到的最重要的参数是`valid_pct=0.2`。告诉fastai保留 20% 的数据，这些数据*完全*不用来*训练模型*。这 20% 的数据被称为*验证集*。剩余 80% 的数据被称为*训练集*。验证集是用来度量模型的精度。默认情况下，这 20% 是随机选择的。我们每次运行此代码时，参数`seed=42`设置 *随机数种子* 为相同的值，意味着我们每次运行代码都会获得相同的验证集 —— 这样，如果我们变更模型并重新训练它，我们就知道任何差异都是由于模型的变更，而不是不同的随机验证集。\n",
    "\n",
    "fastai 会*一直只使用* 验证集来展示你的模型的准确率，*永远不是* 训练集。这至关重要，因为如果你用足够长的时间训练一个足够大的模型，它最终会记住数据集中每条数据的标注信息！结果实际上不是一个有用的模型，因为我们关心的是我们的模型在*之前没有见过的图像*上工作的好不好。创建模型时我们的目标始终都是：训练后的模型，它只对未来看到的数据有用。\n",
    "\n",
    "即使你的模型没有完全记住所有的数据，在刚开始训练时，它可能也已经记住了其中的某些部分。因此，你训练的时间越长，在训练集上得到准确率也就越高，一段时间内验证集的准确率也会改善，但最终情况会变糟，因为模型开始记忆训练集，而不是在数据中寻找可泛化的潜在模式。当这种情况发生时，我们说模型是*过拟合*。\n",
    "\n",
    "下图\\<img_overfit\\>展示了当过拟合时发生的状况，我们使用了只有一个参数的简单例子，基于`x**2`函数随机生成的数据。正如你看到的，尽管过拟合模型的预测在观察数据点附近是准确的，但当超出该范围时，它们就偏离了。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/att_00000.png\" alt=\"Example of overfitting\" caption=\"Example of overfitting\" id=\"img_overfit\" width=\"700\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有的机器学习实践者和所有的算法进行训练时，**过拟合是最重要和极具挑战性的问题**。正如你将看到的，创建一个在训练过的数据上做出好的预测的模型很容易，但在模型以前从未见过的数据上做出精确预测则要困难得多。当然，在实践中这是真正重要的数据。例如，如果您创建了一个手写数字分类器（我们很快就会！），并使用它识别支票上的手写数字，那么你永远不会看到模型所训练的任何数字 —— 支票会处理稍微不同的手写变体。在本书中，你会学到许多避免过拟合的方法。然而，你应该只在确认过拟合实际发生后使用这些方法（即，在训练期间你实际观察验证精度变糟）。我们经常看到实践者在有足够多的数据时，并不需要使用过拟合规避技术，但他们依然在用，最终模型的精度可能会低于应该能够达到的成果。\n"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "> ‼️ 验证集。在训练模型时，必须 _始终_ 同时具有训练集和验证集，并必须只在验证集上度量模型的准确性。如果您没有足够的数据并且训练的时间太长，会发现模型的准确率开始变差。这被称为*过拟合*。fastai默认`valid_pct`为`0.2`，因此即使您忘记，fastai也会为您创建一个验证集！\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练图像识别器代码的第五行代码告诉fastai创建一个 _卷积神经网络_（CNN），并指定使用什么 _架构_（即，创建什么类型的模型），我们想要在什么数据上训练它，以及使用什么 _评价指标_：\n",
    "\n",
    "```python\n",
    "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
    "```\n",
    "\n",
    "为什么是CNN？这是目前创建计算机视觉模型最先进的方法。我们会在这本书学习所有关于CNN如何工作的知识。它们的结构灵感源自于人类视觉系统的工作原理。\n",
    "\n",
    "在fastai中有许多不同架构，我们会在本书介绍（和讨论如何创建你自己的架构）。然而，大多数时候，选择架构并不是深度学习过程中非常重要的部分。这是学者们喜爱谈论的事情，但在实践中，你在这件事情上并不需要花很多时间。一些标准架构在大多数情况下都是有效的，在此用例中，我们使用一种名为 _ResNet_ 的架构，我们在本书中经常会谈到它。对于许多数据集和`问题`来说，它既快速又准确。`ResNet34`中的`34`是指这种架构变体的层数（其它选项有`18`、`50`、`101`和`152`）。使用更多层的架构，模型训练的时间更长，也更容易过拟合（即在验证集的精度开始变糟之前，您不能训练太多轮次）。另一方面，当使用更多的数据时，它们可以更准确一些。\n",
    "\n",
    "什么是评价指标（metric）？_评价指标_ 是使用验证集度量模型预测质量的函数，在每个 _训练轮次_ 结束时显示输出。在本例中，我们使用`error_rate`，它是由 fastai 提供的函数，正如它的命名：告诉你在验证集中被错误分类的图像百分比。另一个常用分类评价指标是`精度（accuracy）`（即`1.0-error_rate`）。fastai提供了更多评价指标，我们会在本书讨论。\n",
    "\n",
    "评价指标的概念可能会让你想到 _loss_，但有它们有重要的区别。loss 的全部目是去定义训练系统能够用于自动更新权重的“性能度量标准”。换句话说，loss 好的选择是随机梯度下降容易使用的选择。但是评价指标是为人类定义的，所以一个好的评价指标对你来说是很容易理解的，并且尽可能接近你希望的模型做什么。有时，你可能会决定损失函数是一个合适的指标，但情况不一定如此。"
   ]
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   "source": [
    "`vision_learner`也有一个`pretrained`参数，默认为`True`（即使我们没有指定它，在本例中它也被使用了），它将你模型中的权重设置为已经被专家训练可以从130万张图像中识别出一千个不同类别（使用著名的[ImageNet数据集](http://www.image-net.org/)）的那些值。一个模型具有在其它数据集上训练出的权重被称为 _预训练模型_。你差不多应该总是使用预先训练模型，因为这意味着你的模型，即使在你向它展示任何数据之前，就已经非常有能力了。正如你将看到的，在深度学习模型中，你会需要这些与你项目的细节几乎无关的能力。例如，预训练模型的部分能力会处理边缘、渐变和颜色检测，这些能力很多任务都需要。\n",
    "\n",
    "当使用预训练模型时，`vision_learner`会移除最后一层，因为该层总是专门针对原始训练任务（即，ImageNet数据集分类）专门定制的，用一个或多个具有随机权重的新层将其替换，大小适合于你正在处理的数据集。模型的最后部分被称为 _head_。\n",
    "\n",
    "使用预训练模型是让我们训练出更精准的模型的*最* 重要的方法，可以更快，更少的数据及更少的时间与成本。你可能会认为这意味着使用预训练模型将是学术深度学习理论中研究最多的领域... 那你就大错特错了！在大多数课程、书籍或软件库功能中，预训练模型的重要通常不会被认可或讨论，在学术论文中也几乎不会考虑。在2020年初我们写这些的时候，事情才刚刚开始改变，但可能还需要一段时间。所以要小心：与你交流的大多数人可能会极大低估你在资源很少的情况下用深度学习中所能做的事情，因为他们可能不会深入理解如何使用预训练模型。\n",
    "\n",
    "使用预训练模型来完成与最初训练不同的任务被称为*迁移学习*。不幸的是，由于对迁移学习的研究不足，很少领域有可用的预训练模型。例如，目前医学中可用的预训练模型很少，使得在该领域使用迁移学习面临挑战。此外，还没有很好的理解把迁移学习用于诸如时间序列分析。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 术语：迁移学习。使用预训练模型来完成与最初训练不同的任务。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 🤖译者：在2023年初我们翻译本书时，情况已发生变化，迁移学习成为该领域的热门研究。"
   ]
  },
  {
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   "source": [
    "我们代码的第六行告诉fastai如何 _拟合_ 模型：\n",
    "```python\n",
    "learn.fine_tune(1)\n",
    "```\n",
    "正如我们所讨论的，架构仅仅描述数学函数的*模板*；在我们为它包含的数百万个参数提供值之前，它实际上不会做任何事情。\n",
    "\n",
    "这是深度学习的关键 —— 确定如何拟合模型参数，让它解决你的问题。为了拟合模型，我们必须提供至少一条信息：看每张图像的次数（称为*轮次*值）。你选择的轮次值很大程度上取决于你有多少可用时间，以及在实践中需要多长时间来拟合你的模型。如果你选择了一个太小的数字，就总是要训练更多的轮次。\n",
    "\n",
    "但为什么这种方法被称为`微调（fine_tune）`，而不是`拟合（fit）`呢？fastai确实有一种称为`拟合`的方法，它确实拟合模型（即，在多次检测训练集中的图像，每次更新参数以使预测越来越接近目标标签）。但在此例中，我们用一个预训练模型开始，我们不想放弃它已经具有的那些能力。正如您会在本书中学到的，有一些重要的技巧为新的数据集调整预训练模型 —— 这个过程称为*微调（fine-tuning）*。"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "> 术语：微调。一种迁移学习技术，预训练模型的参数通过额外的训练轮次被更新，用于与预训练模型不同的任务。"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "当您使用`fine_tune`方法时，fastai将为你使用几个技巧。你可以设置一些参数（我们将在后面讨论），但在此处默认情况下，它做了两步：\n",
    "1. 使用一个训练轮次只拟合模型必要的那部分，让新的随机头（head）与你的数据集正确工作。\n",
    "2. 在调用这个方法时，使用要求的训练轮次值拟合整个模型，更新后期层（尤其是 head）的权重比更新早期的层更快（正如我们将看到的，通常不需要预训练的权重做更多改变）。\n",
    "\n",
    "模型的 _head_ 是针对新数据集新添加的部分。一个*轮次* 是对数据集的一次完整遍历。调用`拟合`后，每个轮次的结果都会被输出，显示训练轮次值、训练集和验证集的损失（loss）（用于训练中模型的“性能度量指标”）以及你请求*评价指标*（本例中为错误率）。\n"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "现在使用这些代码，从标注的事例中模型学习到了识别猫和狗。但它怎么做的呢？"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我们图形分类器学习到了什么"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在此阶段，我们有一个运行良好的图像识别器，但并不知道它在做什么。尽管许多人抱怨深度学习产生难以理解的“黑箱”模型（即可以预测却没有人能理解），但事实并非如此。大量的研究可以让我们深入检查深度学习模型，并从中获得丰富的见解。话虽如此，完全理解各种机器学习模型（包括深度学习和传统统计模型）都非常具有挑战性，尤其是模型在遇到与训练不同的数据会如何表现的问题上。我们将在本书中讨论这个问题。\n",
    "\n",
    "2013年，博士生 Matt Zeiler 和他的导师 Rob Fergus 发表了[卷积网络的可视化和理解](https://arxiv.org/pdf/1311.2901.pdf)，展示了如何将模型在每一层学习到的神经网络权重可视化。他们仔细分析了2012年ImageNet竞赛的获胜模型，并利用这一分析大大改进了模型，从而能够继续赢得2013年的竞赛。下图是他们发布的第一层\\<img_layer1\\>权重。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/layer1.png\" alt=\"Activations of the first layer of a CNN\" width=\"300\" caption=\"Activations of the first layer of a CNN (courtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer1\">"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "这张画需要一些解释。对于每一层，带有浅灰色背景部分的图像，显示的是被重建的权重图像，而图像底部较大的部分显示了训练图像与每组权重最匹配的部分。对于 Layer 1，我们可以看出模型已经发现了表示对角、水平和垂直边界以及各种梯度的权重。（注意，每一层仅显示特征的子集；实际上所有层会有上千个特征。）这些是模型在计算机视觉中学习到的基本构建块。神经科学家和计算机视觉研究人员对它们做了广泛的分析，事实证明这些学习到的构建块与人眼的基本视觉机制非常相似，也与深度学习时代之前研发的手工计算机视觉特征非常相似。下一层\\<img_layer2\\>的展示如下图所示。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/layer2.png\" alt=\"Activations of the second layer of a CNN\" width=\"800\" caption=\"Activations of the second layer of a CNN (courtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer2\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "层2（Layer 2）有九个由模型找到的每个特征权重重建的示例。我们可以看到，该模型已经学会创建特征检测器来查找拐角、重复线、圆圈和其他简单模式。这些是由第一层开发的基本构建块构建的。右侧图像显示了每个与实际图像最匹配特征的小块。例如，第2行第1列中的特定图案与日落相关的渐变和纹理匹配。\n",
    "\n",
    "下图显示\\<img_layer3\\>来自论文，显示了层 3的特征重建结果。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter2_layer3.PNG\" alt=\"Activations of the third layer of a CNN\" width=\"800\" caption=\"Activations of the third layer of a CNN (courtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer3\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如你通过图像右侧所看到的，这些特征现在能够识别并匹配更高级别的语义组件，如车轮、文本和花瓣。使用这些组件，层 4（Layer 4）和层 5（Layer 5）甚至可以识别更高级别的概念，如下图\\<img_layer4\\>所求。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter2_layer4and5.PNG\" alt=\"Activations of layers 4 and 5 of a CNN\" width=\"800\" caption=\"Activations of layers 4 and 5 of a CNN (courtesy of Matthew D. Zeiler and Rob Fergus)\" id=\"img_layer4\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这篇论文研究的是一个被称为 *_AlexNet_* 的老模型，只包含五层。后来的网络开发已经有数百层，所以你可以想象由这些模型开发的特征有多丰富！\n",
    "\n",
    "当之前微调我们的预训练模型时，我们适配了最后几层所关注的内容（花、人类、动物），专门处理猫狗识别问题。通常来说，我们可以在很多不同的任务上专门研究这样的预训练模型。让我们来看几个例子。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图像识别器可以处理非图像类任务"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "顾名思义，图像识别器只能识别图像。但很多事物都可以用图像来表示，这意味着图像识别器可以学习去完成许多任务。\n",
    "\n",
    "例如，声音可以转换为光谱图，光谱图是显示音频文件中每个时间每个频率数量的图表。Fast.ai的学生Ethan Sutin采用这种方法使用8732个城市声音的数据集轻松击败最先进的[环境声音检测模型](https://medium.com/@etown/great-results-on-audio-classification-with-fastai-library-ccaf906c5f52)所公布的精度。正如你在图\\<img_spect\\>中看到的，fastai的`show_batch`清楚地展示了每种不同的声音都有非常独特的光谱图。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"show_batch with spectrograms of sounds\" width=\"400\" caption=\"show_batch with spectrograms of sounds\" id=\"img_spect\" src=\"images/att_00012.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只需在图表上绘制时间序列，就可以轻松地将时间序列转换为图像。然而，尝试以尽可能容易地提取最重要的组件的方式表示你的数据通常是一个好主意。在时间序列中，季节性和异常性等事物最有可能引起兴趣。有各种时间序列数据的有效转换。例如，fast.ai的学生Ignacio Oguiza使用格拉姆角差场（GADF）的技术，从时间序列数据集中创建了用于橄榄油分类的图像。您可以在下图\\<ts_image\\>中看到它的结果。然后，就如你在本章中看到的那样，他将这些图像喂给图像分类模型。尽管只有30个训练集图像，但结果的准确率超过90%，接近最先进的水平。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Converting a time series into an image\" width=\"700\" caption=\"Converting a time series into an image\" id=\"ts_image\" src=\"images/att_00013.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fast.ai学生另一个有趣的项目示例来自Gleb Esman。他在Splunk使用用户鼠标移动和点击的数据集进行欺诈检测。他通过绘制鼠标指针的位置、速度和加速图画，把他们转化为图像，图像里用彩色线条进行显示，并使用[彩色小圆圈](https://www.splunk.com/en_us/blog/security/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.html)表示鼠标点击，如下图所求。然后用本章我们学到的到方法，喂给图像识别模型，由于它的效果非常好，还为这种欺诈分析方法申请了专利!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Converting computer mouse behavior to an image\" width=\"450\" caption=\"Converting computer mouse behavior to an image\" id=\"splunk\" src=\"images/att_00014.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个例子来自Mahmoud Kalash等人的论文[“利用深度卷积进行恶意软件分类”](https://ieeexplore.ieee.org/abstract/document/8328749)，解释了“恶意软件二进制文件被划分为八位序列，然后再将其转换为相应的十进制值。这个十进制向量被重新变形，生成了表示恶意软件样本的灰度图像，” 如下图\\<malware_proc\\>所示。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Malware classification process\" width=\"623\" caption=\"Malware classification process\" id=\"malware_proc\" src=\"images/att_00055.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，作者展示了不同类别的恶意软件通过这一过程所生成的“图片”，如下图\\<malware_eg\\>所示。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Malware examples\" width=\"650\" caption=\"Malware examples\" id=\"malware_eg\" src=\"images/att_00056.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如你所看到的，对人眼来说不同类型恶意软件看起来差别很大。研究人员基于这种图像表示训练出来的恶意软件分类模型比之前在任何学术文献中所展示的方法都更准确。这为数据集转换为图像表示提供了一个很好的经验法则：如果人眼可以从图像中识别类别，那么深度学习模型也应该能够做到。\n",
    "\n",
    "一般来说，如果你在如何表示你的数据方面有些创新的话，你会发现深度学习中的少量通用方法非常有效。你不要这里描述的内容视作“黑客变通法”，因为他们通常（正如这些方法一样）会击败以前最先进的结果。这确实是思考这些问题领域的正确方法。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 术语回顾"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们刚刚涵盖了很多信息，让我们简单的回顾一下，表\\<dljargon\\>提供了一个深度学习词汇表。\n",
    "|     名词     |                     含义                     |\n",
    "| :-------------------- | :------------------------------------------ |\n",
    "|   标签(label)    |         我们尝试预测的数据，例如“狗”或“猫”          |\n",
    "|   架构(architecture)    |         我们尝试去拟合的模型的 _模板_；我们传递输入数据和参数的实际数学的函数 |\n",
    "|模型(model) |带有具体一组参数的架构的组合 |\n",
    "|参数(parameters) |模型中的值，随着它能做的任务而变化，并通过模型训练进行更新 |\n",
    "|拟合(fit) |更新模型的参数，使得模型用输入数据所得到的预测与目标标注一致 |\n",
    "|训练(train) |_拟合(fit)_ 的同义词 |\n",
    "|预训练模型(pretrained model) |   一个通常已经使用大数据集被训练过的模型，并且会被微调 |\n",
    "|微调(fine-tune) |为不同的任务更新预训练模型 |\n",
    "|轮次(epoch) |对输入数据的一次完整遍历 |\n",
    "|损失(loss) |模型如何好的指标，选择通过SGD驱动训练 |\n",
    "|指标(metric) |使用为人类消费选择的验证集来衡量模型有多好 |\n",
    "|验证集(validation set) |从训练中拿出的一组数据，仅用于衡量模型有多好 |\n",
    "|训练集(training set) |用于拟合模型的数据；不包括验证集的任何数据 |\n",
    "|过拟合(overfitting) |用某种方法训练模型， _记住_ 输入数据的具体特征，而不是在训练期间很好的泛化到没有见过的数据 |\n",
    "|卷积神经网络（CNN） |卷积神经网络，一种特别适用于计算机视觉任务的神经网络 |"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "掌握了这些名词，我们现在就能将迄今为止介绍的所有关键概念汇集在一起。花点时间回顾这些定义，并阅读下面的总结。如果你能理解的话，就为理解接下来的讨论做好了准备。\n",
    "\n",
    "*机器学习* 是一门学科，我们定义的程序不是完全自己编写，而是通过从数据学习。*深度学习* 是使用多层 *神经网络* 的机器学习中的一个hw专业。*图像分类*（也称为*图像识别*）是个代表性例子。我们用 *标注的数据* 开始；即，我们为每张图像分配 *标签*的一组图像，说明他们表示什么。我们的目标是产生一个程序，被称为*模型*，对给定的新图像所表示的内容做出准确的*预测*。\n",
    "\n",
    "每个模型都从 *架构* 的选择开始，这是一种模型内部如何进行工作的通用模板。*训练*（或 *拟合*）模型的过程就是寻找一组 *参数值*（或*权重*）的过程，他们可以把通用架构转换为一个适用于我们特定类型数据的模型。为了定义模型在单一预测上有多好，我们需要定义一个 *损失函数*，可以用来判定模型好坏的得分。\n",
    "\n",
    "为了训练得更快，可以用一个 *预训练模型* 开始 —— 一个已经在其他数据上训练过的模型。通过在我们的数据上做多一点训练来使其适配我们的数据，这一过程称为 *微调*。\n",
    "\n",
    "当我们训练模型时，一个关键概念是确保我们模型的 *泛化*。即，它从我们的数据中学习通用经验，同时也适用于它将遇到的新数据，这样它能在那些数据上做出很好的预测。风险是如果我们的模型训练得不好，它有效地记住它见过的内容，而它不是学习通用的经验，然后它就会对新图像做出差的预测。这样的失败被称为 *过拟合*。为了避免这种情况，我们总是将数据分为 *训练集* 和 *验证集* 两部分。我们只展示训练集训练模型，然后通过验证集上的数据看模型性能怎么样，来我们评估模型表现如何。通过这种方式，我们检查模型从训练集学习到的经验是否可以泛化到验证集。为了让人们评估模型在整体验证集上的表现如何，我们定义了 *指标*。在训练过程中，当模型已经看了训练集中的每项数据时，我们称它为一个 *轮次* 。\n",
    "\n",
    "所有这些概念都适用于一般的机器学习。即，它们适用于通过用数据训练模型来定义模型的各种方案。使得深度学习与众不同的是特定类型的架构：基于 *神经网络* 的架构。特别是，图像分类任务严重依赖 *卷积神经网络*，我们稍后会讨论。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习不是用于只图像分类"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "近年来，深度学习在图像分类方面的有效性得到了广泛讨论，甚至在复杂任务上也展示出了 *超人* 的效果，如在 CT 扫描中识别恶性肿瘤等。但正如我们将要展示的，它可以做的远不止这些。\n",
    "\n",
    "例如，让我们谈谈对自动驾驶车辆来说至关重要的事情：在图像中定位物体。如果自动驾驶汽车不知道行人在哪里，它就不知道如何避开行人。创建一个模型能识别每个独立像素的内容被称为 *分割*。下面是我们如何用fastai在[*Camvid* 数据集](http://www0.cs.ucl.ac.uk/staff/G.Brostow/papers/Brostow_2009-PRL.pdf)的子集上训练一个分割模型，这个数据集它来自于Gabriel J.Brostow, Julien Fauqueur, and Roberto Cipolla的论文“视频中的语义对象类：一个高清真实数据库”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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    "path = untar_data(URLs.CAMVID_TINY)\n",
    "dls = SegmentationDataLoaders.from_label_func(\n",
    "    path, bs=8, fnames = get_image_files(path/\"images\"),\n",
    "    label_func = lambda o: path/'labels'/f'{o.stem}_P{o.suffix}',\n",
    "    codes = np.loadtxt(path/'codes.txt', dtype=str)\n",
    ")\n",
    "\n",
    "learn = unet_learner(dls, resnet34)\n",
    "learn.fine_tune(8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们不打算逐行查看这段代码，因为它与我们之前的例子几乎完全相同！（不过我们会在第十五章深入研究分割模型 ，及在本章中简要介绍的所有其他模型，以及更多的模型。）\n",
    "\n",
    "通过让模型对图像中的每个像素进行颜色编码，我们能可视化它完成任务的情况。正如您所看到的，它几乎完美地分类了每个物体中的每个像素。例如，注意所有的汽车都用相同的颜色覆盖，所有的树都用相同的颜色覆盖（在每对图像中，左边的图像是真实标注，右边的是来自模型的预测）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results(max_n=6, figsize=(7,8))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在过去的几年里，另一个深度学习取得巨大进步的领域是自然语言处理（NLP）。计算机现在可以生成文本，把一种语言自动翻译为另一种语言，分析评论，标注句子中的单词等等。下面是训练一个模型的全部代码，它能够对影评观点进行分类，优于五年前存在的所有模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.579781</td>\n",
       "      <td>0.540394</td>\n",
       "      <td>0.731760</td>\n",
       "      <td>04:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='1' class='' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      25.00% [1/4 08:53&lt;26:40]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.284717</td>\n",
       "      <td>0.240007</td>\n",
       "      <td>0.902160</td>\n",
       "      <td>08:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "      <progress value='193' class='' max='781' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      24.71% [193/781 01:37&lt;04:58 0.2478]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.text.all import *\n",
    "\n",
    "dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')\n",
    "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)\n",
    "learn.fine_tune(4, 1e-2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清空 \n",
    "如何运行这个单元后遇到“CUDA内存不足错误”，点击菜单中的 Kernel 然后点击 Restart Kernel，进行内核重启。不要执行上面单元的代码，复制并粘贴下面代码：\n",
    "```\n",
    "from fastai.text.all import *\n",
    "\n",
    "dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test', bs=32)\n",
    "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)\n",
    "learn.fine_tune(4, 1e-2)\n",
    "```\n",
    "\n",
    "它会将训练批次的大小减少为32（稍后我们会解释）。假如你还是遇到同样的错误，将32改为16。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此模型使用来自 Andrew Maas 等人论文“学习情感分析的单词向量”中的[“IMDB大影评数据集”](https://ai.stanford.edu/~ang/papers/acl11-WordVectorsSentimentAnalysis.pdf)。模型可以很好地处理数千字的影评，不过让我们在一个简短的评论上测试它，来看它是如何做的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('pos', tensor(1), tensor([1.8377e-04, 9.9982e-01]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(\"I really liked that movie!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里，我们可以看到模型认为这是正面的评论。输出结果的第二部分是在我们数据词汇中的“pos”的索引，最后一部分是归因到每个分类的概率（“pos”为99.982%，“neg”为0.018%）。\n",
    "\n",
    "现在轮到你了！写下你自己的简短影评，或者从互联网上复制一个，就能够看到这个模型对它的看法。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边栏：顺序很重要"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 Jupyter 中，你执行每个单元格的顺序非常重要。它不像 Excel，一旦你在某处输入内容，所有内容都会得到更新 —— 它有一个内部状态，每次你执行一个单元就会得到更新。例如，当您运行 notebook 的第一个单元格（带有“ Click Me ”注释）时，你创建了一个名为`learn`的对象，包含图像分类问题的一个模型和数据。如果我们直接运行刚刚单元的内容（预测评论是好还是坏的），你会获得一个错误，因为这个`learn`对象不含文本分类模型。这个单元需要在下面这个内容之后运行：\n",
    "```python\n",
    "from fastai.text.all import *\n",
    "\n",
    "dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')\n",
    "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5,\n",
    "                                metrics=accuracy)\n",
    "learn.fine_tune(4, 1e-2)\n",
    "```\n",
    "输出本身可能具有欺骗性，因为它们包含了最后一次执行单元的结果。如果更改单元中的代码，而没有执行它，则老的（误导性的）结果会保留。\n",
    "\n",
    "除非我们明确地提及，否则在[本书网站](https://book.fast.ai/)上的notebooks应该按照从上到下的顺序运行。一般来说，在做实验时，你会发现自己以任意顺序执行单元会很快（这是Jupyter Notebook超级简洁的特性），但是一旦你完成了探索，并完成了最终版本的版本，确保你能够按顺序运行 notebooks 中的单元（不然将来你自己不一定还记得那条复杂路径！）\n",
    "\n",
    "在命令模式下，按两次 `0` 将重启 *内核（kernel）*（即 notebook 的引擎开关）。这会清除你的状态，使其恢复到刚刚启动 notebook 时的状态。选择 “Run —— Run All Above Selected Cell”菜单，运行你所在点之上的所有单元格。在开发 fastai 库时，我们发现这个功能很有用。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边栏结束"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你有任何关于 fastai 方法的问题，你可以使用 `doc` 函数，给它传递方法名：\n",
    "```python\n",
    "doc(learn.predict)\n",
    "```\n",
    "它会弹出一个包含内容的小窗口，如下所求：\n",
    "\n",
    "<img src=\"images/doc_ex.png\" width=\"600\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`doc` 提供了一行简短的说明。点击“Show in docs”链接可以查看完整文档了解所有细节和大量实例。此外，fastai的大多数方法只有几行代码，您可以点击“ source ”链接来查看后台到底发生了什么。\n",
    "\n",
    "我们继续讨论一些不那么吸引人，但可能特别具有商业用途的东西：从普通 *表格* 数据构建模型。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 术语：表格。表格形式的数据，如电子表格、数据库或者 CSV 文件。表格模型是根据表中其他列的信息预测某一列的模型。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "事实证明，这看起来也非常相似。下面是训练一个模型所需的代码，该模型可以根据一个人的社会经济背景来预测他是否是高收入者："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.tabular.all import *\n",
    "path = untar_data(URLs.ADULT_SAMPLE)\n",
    "\n",
    "dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names=\"salary\",\n",
    "    cat_names = ['workclass', 'education', 'marital-status', 'occupation',\n",
    "                 'relationship', 'race'],\n",
    "    cont_names = ['age', 'fnlwgt', 'education-num'],\n",
    "    procs = [Categorify, FillMissing, Normalize])\n",
    "\n",
    "learn = tabular_learner(dls, metrics=accuracy)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如您所看到的，我们必须告诉fastai哪些列是*分类的*（即，包含一组选定的离散值，如`occupation`），哪些列是*连续的*（即，包含表示数量的数值，如`age`）。\n",
    "\n",
    "这里没有可用于此任务的预训练模型（通常，许多表格建模任务的预训练模型无法广泛获得，尽管一些组织已经创建了供内部使用的模型），在这种情况下，我们不使用`fine_tune`，而是使用`fit_one_cycle`，这是从`零`开始训练 fastai 模型的最常用方法（即，没有迁移学习）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.374053</td>\n",
       "      <td>0.372600</td>\n",
       "      <td>0.828471</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.371670</td>\n",
       "      <td>0.362348</td>\n",
       "      <td>0.833999</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.350932</td>\n",
       "      <td>0.359070</td>\n",
       "      <td>0.838145</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "learn.fit_one_cycle(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此模型所使用的[Adult dataset](http://robotics.stanford.edu/~ronnyk/nbtree.pdf) ，来自 Rob Kohavi 的论文“提高朴素贝叶斯分类器的准确率：混合决策树”，这个数据集包含很多个人的统计数据（如他们的教育、婚姻状况、种族、性别以及年收入是否超过5万美元）。该模型训练时间约为10秒，准确率超过80%。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们再看一个。推荐系统非常重要，尤其是在电子商务中。亚马逊（Amazon）和网飞（Netflix）等公司竭尽全力推荐用户可能喜欢的产品或电影。下面展示了使用[MovieLens dataset](https://doi.org/10.1145/2827872)数据集如何训练一个模型，该模型可以根据人们以前的观影习惯来预测他们可能喜欢的电影。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "</table>"
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       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.378706</td>\n",
       "      <td>1.353837</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.283051</td>\n",
       "      <td>1.182615</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.051507</td>\n",
       "      <td>0.870182</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.827181</td>\n",
       "      <td>0.705093</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.702141</td>\n",
       "      <td>0.661779</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.646693</td>\n",
       "      <td>0.647653</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.621587</td>\n",
       "      <td>0.642445</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.605313</td>\n",
       "      <td>0.639461</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.602502</td>\n",
       "      <td>0.638560</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.606968</td>\n",
       "      <td>0.638429</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.collab import *\n",
    "path = untar_data(URLs.ML_SAMPLE)\n",
    "dls = CollabDataLoaders.from_csv(path/'ratings.csv')\n",
    "learn = collab_learner(dls, y_range=(0.5,5.5))\n",
    "learn.fine_tune(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该模型在0.5到5.0的范围内预测电影评级，平均错误大约在0.6。由于我们预测的是连续的数字，而不是类别，因此我们必须使用参数`y_range`来告诉 fastai 我们的目标范围。\n",
    "\n",
    "尽管我们没有实际使用预训练模型（与表格模型没有使用的原因相同），但 fastai 允许我们在本例中使用`fine_tune`（你会在第五章中学习它如何及为什么工作)。有时，最好将`fine_tune`和`fit_one_cycle`进行对比实验，来看在你的数据上哪个工作最好。\n",
    "\n",
    "我们可以调用`show_results`来查看一些用户ID、电影ID、实际评级和预测评级的示例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.650622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.435779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.833302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.840652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.797843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.272635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>93.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.155020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.304728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边栏：数据集：模型的食物"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您已经在本节中看到了不少模型，每个都使用不同的数据集进行训练去做不同的任务。在机器学习和深度学习中，没有数据我们什么都做不了。因此，为我们创建数据集去训练我们的模型都是英雄（通常却是被低估的）。一些最有用和重要的数据集变成了重要的*学术基线*；即，被研究人员广泛研究并用于比较算法的变化的数据集。一些成为家喻户晓的名字（至少在训练模型中家喻户晓），如 MNIST、CIFAR-10 和 ImageNet。\n",
    "\n",
    "本书中被使用的数据集被选中是因为他们提供了你可能遇到的最好的数据类型示例，而且在学术文献中也有许多使用这些数据集的模型结果例子，你能够对比你的工作。\n",
    "\n",
    "本书中使用的大多数数据集花费了创建大量的工作去构建。例如，在本书中稍后我们会向你展示如何创建一个可以在法语和英语之间进行翻译的模型。关键输入是法语/英文并行文本语料，在2009年由由宾夕法尼亚大学 Chris Callison-Burch 教授准备的。这个数据集包含超过两千万法语和英文的句子对。他以一种非常聪明的方式创建数据集：通过爬取数百万个加拿大网页（通常是多语言网页），然后使用一组简单的启发式方法将法语内容的 URL 转换为指向英文相同内容的 URL 。\n",
    "\n",
    "当你查看这本书的数据集时，想想它们可能来自哪里，以及它们可能是如何策划的。然后考虑你能够为你自己的项目创建哪些有趣的数据集（很快我们就会带你一步一步地实现创建你自己的图像数据集的过程）。\n",
    "\n",
    "fast.ai花费了大量时间创建流行数据集的精简版本，这些数据集是支持快速原型开发和实验专门设计的，并且更易于学习。在本书中，我们通常会先使用一个精简版，然后再扩展到完整版（就像我们在本章中所做的那样！）。实际上，这就是世界顶级从业人员在实践中进行建模的方法。他们用他们的数据子集做大部分实验和原型，只有他们已经很好的理解了他们必要做的内容才使用完整的数据集。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 边栏"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们训练的每一个模型都展示了训练损失和验证损失。一个好的验证集是训练过程中最重要的部分。让我们来看看为什么及学习如何创建它。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 验证集和测试集"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如我们之前讨论的，模型的目标是对数据进行预测。但从根本上说，模型训练过程是简易的。如果用我们所有的数据训练模型，然后用同样的数据去评价模型，将无法判断该模型在没有见过的数据上表现如何。如果没有这个有价值的信息来指导我们训练模型，它很有可能会变得善于对这些数据进行预测，但在新的数据上表现不佳。\n",
    "\n",
    "为了避免这种情况，首先需要将数据集分为两个集合：*训练集*（模型在训练中看到的）和*验证集*，也称为*开发集*（仅用于评价）。可以让我们测试模型是否将从训练数据中学习到的经验泛化到新的数据，也就是验证数据。\n",
    "\n",
    "理解这种情况的一种方式是，从某种意义上说，我们不希望模型通过“作弊”来获得好的结果。如果它对一个数据项做出了准确的预测，那应该是因为它已经学习了这种数据项的特征，而不是因为模型被*已经见过的那些特定数据项* 所限定。\n",
    "\n",
    "分离出我们的验证数据，意味着模型从未在训练中见过它，因此完全不受验证数据的影响，所以无论如何也不会作弊。对吗？\n",
    "\n",
    "实际上未必，这很微妙。因为在实际场景中，我们几乎不能仅仅训练一次模型的权重参数来创建模型。相反，建模与网络架构、学习率、数据增强策略，及我们在后续章节中要讨论的其他因素相关，我们会根据不同的建模选择开发很多个版本的模型。其中某些选项被描述为 *超参数（hyperparameters）* 的选项。该词反映出他们是参数的参数，因为他们是控制权重参数含义的更高级别的选项。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问题是，普通的训练过程在学习权重参数值时只查看训练数据的预测，但我们的情况并非如此。作为建模者，在我们决定探索新的超参数值时，会通过查看验证数据上的预测来评估模型！因此，模型的后续版本是通过我们查看了验证数据后间接形成的。正如自动训练过程存在过拟合训练数据的风险一样，人工试错和探索过程也存在过拟合验证数据的风险。\n",
    "\n",
    "这个难题的解决方案是引入另一个更高级别的保留数据*测试集*。正如为训练过程中保留验证数据一样，我们还要为自己保留测试集数据。它不能用于优化模型；只能用来评估我们的最终模型。实际上，根据我们希望在训练和建模过程中隐藏数据的程度，定义了数据切割的层次结构：训练数据完全暴露，验证数据较少暴露，测试数据完全隐藏。这种层次结构平行于不同类型的建模和评估过程本身 —— 使用反向传播的自动训练过程，在训练会话之间尝试不同超参数的手动处理，以及对最终结果的评估过程。\n",
    "\n",
    "测试集和验证集应该有足够的数据，以确保您可以很好地估计模型的准确性。例如，如果您正在创建一个猫检测器，通常需要在验证集中至少有30只猫的图像。这意味着如果你有一个包含数千个数据项的数据集，使验证集默认占20%，可能会超出您的需要。另一方面，如果你有大量的数据，使用其中的一些数据来进行验证可能不会带来任何负面影响。\n",
    "\n",
    "拥有两个级别的“保留数据”（验证集和测试集，其中一个级别表示实际上您对自己隐藏的数据），可能看起来有点极端。但这通常是必要的，因为模型往往倾向于用最简单的方法来做出好的预测（记忆），而我们作为易犯错误的人类，往往倾向于自欺欺人地认为我们的模型表现得有多好。测试集的约束可以帮助我们理性的保持诚实。这并不意味着我们 *总是* 需要一个单独的测试集（如果只有少量的数据，可能只需要一个验证集），但一般来说，如果可能的话，最好有一个。\n",
    "\n",
    "如果您打算雇用第三方来代表您执行建模工作，同样的约束也是至关重要的。第三方或许不能准确地理解您的要求，或者他们的动机甚至可能鼓励他们产生误解。一个好的测试集能够很大程度上规避这些风险，并可以让您评估他们的工作是否解决了您的实际问题。\n",
    "\n",
    "坦率地说，如果您是组织中的高级决策者（或者为高级决策者提供建议），那么最重要的收获是：如果您确保真正理解了什么是测试集和验证集，以及它们为什么如此重要，那么当组织决定使用人工智能时，您会避开我们所见过的最大失败根源。例如，如果您正在考虑引入外部供应商或服务，请确保您保留了一些供应商 *永远不会看到* 的测试数据。然后 *您* 在测试数据上检查他们的模型，使用实践中对 *您* 真正重要的评价指标，然后 *您* 决定什么级别的性能是达到要求的。（您自己尝试简单基准也是一个好主意，这样您就知道一个真正简单的模型可以实现什么。通常，您的简单模型的性能与外部“专家”生成的模型一样好！）"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义测试集时加入判断"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要很好地定义验证集（可能还有测试集），有时候您要做的不仅仅是随机抓取原始数据集的一部分。记住：验证集和测试集的一个关键特性是它们必须能够代表您将在未来看到的新数据。这听起来像是一个不可能实现的要求。根据定义，你还没有见到这些数据。但你通常还是知道一些内容的。\n",
    "\n",
    "看几个有启发性的例子。这些例子中有许多来自 [Kaggle](https://www.kaggle.com/)平台的预测建模竞赛，很好地代表了您可能在实践中遇到的问题和方法。\n",
    "\n",
    "如果您正在查看时间序列数据。对于时间序列，选择数据的随机子集太容易了（您可以查看所要预测的日期之前和之后的数据），但不能代表大多数业务用例（您使用历史数据来构建供将来使用的模型）。如果您的数据包括日期，并且您正在构建一个在将来使用的模型，则您应该选择最新日期的连续部分作为您的验证集（例如，最近两周或上个月的可用数据）。\n",
    "\n",
    "假设您要将下图中的时间序列数据拆分为训练集和验证集。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/timeseries1.png\" width=\"400\" id=\"timeseries1\" caption=\"A time series\" alt=\"A serie of values\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机子集是一个糟糕的选择（太容易填补空白，而且不代表你在生产中会需要它），如下图所示。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/timeseries2.png\" width=\"400\" id=\"timeseries2\" caption=\"A poor training subset\" alt=\"Random training subset\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相反，使用前面的数据作为训练集（后面的数据作为验证集），如下图所示。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/timeseries3.png\" width=\"400\" id=\"timeseries3\" caption=\"A good training subset\" alt=\"Training subset using the data up to a certain timestamp\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例如，Kaggle有一场比赛是[预测厄瓜多尔杂货连锁店的销售额](https://www.kaggle.com/c/favorita-grocery-sales-forecasting)。Kaggle的训练数据从2013年1月1日到2017年8月15日，测试数据从2017年8月16日到2017年8月31日。通过这种方式，竞赛组织者确保参赛者从其模型的角度对 *未来* 的时间段进行预测。这类似于量化对冲基金交易员进行 *回溯测试*，以检查他们的模型是否能根据过去的数据对未来做出预测。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二种常见的情况，可以很容易地预料到，在生产中进行预测的数据可能与用来训练模型的数据有 *质的不同*。\n",
    "\n",
    "在Kaggle[分心驾驶员比赛](https://www.kaggle.com/c/state-farm-distracted-driver-detection)中，自变量是坐在方向盘前驾驶员的图像，因变量是发短信，吃东西，或安全地向前看。很多都是相同驾驶员在不同位置的图像，如下图所求。如果您是一家保险公司，根据这些数据构建模型，您最感兴趣的是该模型在从未见过的驾驶员行为上的表现（因为您可能只有一小部分人的训练数据）。认识到这一点，比赛的测试数据由没有出现在训练集中的人的图像组成。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/driver.PNG\" width=\"600\" id=\"img_driver\" caption=\"Two pictures from the training data\" alt=\"Two pictures from the training data, showing the same driver\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假如您将上图中的一张图像放到训练集里，将另一张图像放到验证集里，您的模型将很容易对验证集中的图像做出预测，所以它似乎比在新人身上表现得更好。另一种观点是，如果您在训练模型时使用了所有的人，模型可能会对这些具体的人的细节过拟合，而不仅仅是学习他们的状态（发短信、吃东西等）。\n",
    "\n",
    "类似的方式也在[Kaggle渔业竞赛](https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring)中，通过识别被渔船捕捉的鱼的种类，以减少对濒危种群的非法捕捞。测试集中的渔船没有在训练数据中出现过。这表示验证集中的渔船应该不在训练集中。\n",
    "\n",
    "有时可能不是很清楚验证数据会有什么不同。例如，对于使用卫星图像的问题，您是需要收集更多只是包含某些地理位置信息的训练集，还是来自地理上分散的数据。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在您已经知道怎样构建模型，您可以决定下一步要深入研究些什么了。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择您自己的冒险"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果您想了解更多关于如何在实践中使用深度学习模型的内容，包括如何识别和修复错误，如何创建Web应用程序，以及如何避免您的模型对自身组织或社会造成无法意料的损害，请继续阅读接下来的两章。如果您想从深度学习内部工作机制的基本原理开始学习，请直接学习第四章。（你小时候读过 *_选择你自己的冒险_* 吗？是的，这有点像... 除了比那本书系列包含更多的深度学习。）\n",
    "\n",
    "您需要阅读所有章节才会取得更大进展，但阅读的顺序完全取决于您自己。他们并不互相依赖。如果您跳到第四章，我们会在最后提醒您，在继续往下学习之前，先回来阅读您跳过的章节。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 练习题"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在阅读了一页又一页的文章之后，很难知道您真正需要关注和记住的关键事项。所以，我们在每一章结束时准备了一个问题清单和建议完成的步骤。所有的答案都在这一章的正文中，所以如果有任何不确定的问题，请重读相关的正文，并确保理解了它。所有问题的答案也可以在[本书网站](https://book.fast.ai)找到。当遇到困难的时候您也可以通过访问[论坛](https://forums.fast.ai)，从其他学习本书的伙伴那里得到帮助。\n",
    "\n",
    "对于更多问题，包括详细解答和视频链接，请访问：Radek Osmulski 的 [aiquizzes](http://aiquizzes.com/howto)。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 您进行深度学习时需要下列内容吗？\n",
    "   - 大量的数学知识   是/否\n",
    "   - 海量数据        是/否\n",
    "   - 大量昂贵的算力   是/否\n",
    "   - 博士学位        是/否\n",
    "2. 列举当今深度学习表现最佳的五个领域。\n",
    "3. 基于人工神经元原理的第一个装置叫什么名字？？\n",
    "4. 根据同名书籍，并行分布式处理（PDP）的必备条件是什么？\n",
    "5. 是哪两个理论上的误解制约了神经网络领域的发展？\n",
    "6. 什么是GPU？\n",
    "7. 打开一个notebook并执行一个单元格包含：`1+1`，看看会发生什么？\n",
    "8. 对本章节的notebook精简版的每个单元格进行操作。在执行每个单元格之前，猜猜会发生什么。\n",
    "9. 完成 Jupyter notebook 在线附录。\n",
    "10. 为什么很难使用传统的计算机程序来识别照片中的图像？\n",
    "11. 塞缪尔所说的“权重分配”是什么意思？\n",
    "12. 在深度学习中，我们通常使用什么术语来表示塞缪尔所说的“权重”？\n",
    "13. 画一张图，概括塞缪尔对机器学习模型的观点。\n",
    "14. 为什么很难理解深度学习模型为什么会做出特定的预测？\n",
    "15. 表明神经网络能以任何精度解决任何数学问题的定理叫什么？\n",
    "16. 训练一个模型需要什么？\n",
    "17. 反馈回路如何影响预测警务模型的推广？\n",
    "18. 对于猫识别模型，我们是否总是必须使用 224×224 像素的图像？\n",
    "19. 分类和回归的区别是什么？\n",
    "20. 什么是验证集？什么是测试集？我们为什么需要他们？\n",
    "21. 如果您不提供验证集，fastai 会怎么做？\n",
    "22. 我们是否可以始终使用随机样本作为验证集？为什么或为什么不？\n",
    "23. 什么是过拟合？举个例子。\n",
    "24. 什么是评价指标？它与“损失”有何不同？\n",
    "25. 预训练模型能够提供怎样的帮助？\n",
    "26. 模型的“头”是什么？\n",
    "27. CNN 前面的层发现了哪些类型的特征？后面的层呢？\n",
    "28. 图像模型只用于照片吗？\n",
    "29. 什么是“架构”？\n",
    "30. 什么是分割？\n",
    "31. y_range 有何用途？我们什么时候需要它？\n",
    "32. 什么是“超参数”？\n",
    "33. 在组织中使用人工智能时，避免失败的最佳方法是什么？"
   ]
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    "### 进一步研究"
   ]
  },
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    "每一章还有一个“进一步研究”部分，会提出一些在正文中没有完全回答出来的问题，或者给出一些更高级的作业。本书的网站上没有这些问题的答案，您需要自己做些研究。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 为什么GPU对深度学习有用？CPU有什么不同，为什么它对深度学习不那么有效？\n",
    "2. 试着想想反馈循环可能影响机器学习使用的三个领域。看看你是否能找到在实践中发生的那些被记录下来的例子。"
   ]
  }
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